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Test Bias

LAST UPDATED: 05.22.15

Educational tests are considered biased if a test design, or the way results are interpreted and used, systematically disadvantages certain groups of students over others, such as students of color, students from lower-income backgrounds, students who are not proficient in the English language, or students who are not fluent in certain cultural customs and traditions. Identifying test bias requires that test developers and educators determine why one group of students tends to do better or worse than another group on a particular test. For example, is it because of the characteristics of the group members, the environment in which they are tested, or the characteristics of the test design and questions? As student populations in public schools become more diverse, and tests assume more central roles in determining individual success or access to opportunities, the question of bias—and how to eliminate it—has grown in importance.

There are a few general categories of test bias:

Construct-validity bias refers to whether a test accurately measures what it was designed to measure. On an intelligence test, for example, students who are learning English will likely encounter words they haven’t learned, and consequently test results may reflect their relatively weak English-language skills rather than their academic or intellectual abilities.

Content-validity bias occurs when the content of a test is comparatively more difficult for one group of students than for others. It can occur when members of a student subgroup, such as various minority groups, have not been given the same opportunity to learn the material being tested, when scoring is unfair to a group (for example, the answers that would make sense in one group’s culture are deemed incorrect), or when questions are worded in ways that are unfamiliar to certain students because of linguistic or cultural differences. Item-selection bias, a subcategory of this bias, refers to the use of individual test items that are more suited to one group’s language and cultural experiences.

Predictive-validitybias (or bias in criterion-related validity) refers to a test’s accuracy in predicting how well a certain student group will perform in the future. For example, a test would be considered “unbiased” if it predicted future academic and test performance equally well for all groups of students.

Test bias is closely related to the issue of test fairness—i.e., do the social applications of test results have consequences that unfairly advantage or disadvantage certain groups of student? College-admissions exams often raise concerns about both test bias and test fairness, given their significant role in determining access to institutions of higher education, especially elite colleges and universities. For example, female students tend to score lower than males (possibly because of gender bias in test design), even though female students tend to earn higher grades in college on average (which possibly suggests evidence of predictive-validity bias).

To cite another example, there is evidence of a consistent connection between family income and scores on college-admissions exams, with higher-income students, on average, outscoring lower-income students. The fact that students can boost their scores considerably with tutoring or test coaching adds to the perception of socioeconomic unfairness, given that test preparation classes and services may be prohibitively expensive for many students. (Concerns about bias and unfairness are one contributing factor in a trend toward “test-optional” or “test-flexible” collegiate admissions policies.)

The following are several representative examples of other factors that can give rise to test bias:

If the staff developing a test is not demographically or culturally representative of the students who will take the test, test items may reflect inadvertent bias. For example, if test developers are predominantly white, upper-middle-class males, the resulting test could, due to cultural oversights, advantage demographically similar test takers and disadvantage others.

Norm-referenced tests (or tests designed to compare and rank test takers in relation to one another) may be biased if the “norming process” does not include representative samples of all the tested subgroups. For example, if test developers do not include linguistically, culturally, and socioeconomically diverse students in the initial comparison groups (which are used to determine the norms used in the test), the resulting test could potentially disadvantage excluded groups.

Certain test formats may have an inherent bias toward some groups of students, at the expense of others. For example, evidence suggests that timed, multiple-choice tests may favor certain styles of thinking more characteristic of males than females, such as a willingness to risk guessing the right answer or questions that reflect black-and-white logic rather than nuanced logic.

The choice of language in test questions can introduce bias, for example, if idiomatic cultural expressions—such as “an old flame” or “an apples-and-oranges comparison”—are used that may be unfamiliar to recently arrived immigrant students who may not yet be proficient in the English language or in American cultural references.

Tests may be considered biased if they include references to cultural details that are not familiar to particular student groups. For example, a student who recently immigrated from the Caribbean may never have experienced winter, snow, or a snow-related school cancellation, and may therefore be thrown off by an essay question asking him or her to describe a snow-day experience.

Another aspect of culturally biased testing is implicated in the overrepresentation of black students, especially black males, in special-education programs. For example, the concern is that the tests used to identify students with disabilities, including intelligence tests, are misidentifying black students as learning disabled because of inherent racial and cultural biases.

Reform

As with measurement error, some degree of bias and unfairness in testing may be unavoidable. The inevitability of test bias and unfairness are among the reasons that many test developers and testing experts caution against making important educational decisions based on a single test result. The Standards for Educational and Psychological Testing—a set of proposed guidelines jointly developed by the American Educational Research Association, American Psychological Association, and the National Council on Measurement in Education—include a recommendation that “in elementary or secondary education, a decision or characterization that will have a major impact on a test taker should not automatically be made on the basis of a single score.”

Given the fact that test results continue to be widely used when making important decisions about students, test developers and experts have identified a number of strategies that can reduce, if not eliminate, test bias and unfairness. A few representative examples include:

Striving for diversity in test-development staffing, and training test developers and scorers to be aware of the potential for cultural, linguistic, and socioeconomic bias.

Having test materials reviewed by experts trained in identifying cultural bias and by representatives of culturally and linguistically diverse subgroups.

Ensuring that norming processes and sample sizes used to develop norm-referenced tests are inclusive of diverse student subgroups and large enough to constitute a representative sample.

Eliminating items that produce the largest racial and cultural performance gaps, and selecting items that produce the smallest gaps—a technique known as “the golden rule.” (This particular strategy may be logistically difficult to achieve, however, given the number of racial, ethnic, and cultural groups that may be represented in any given testing population).

Screening for and eliminating items, references, and terms that are more likely to be offensive to certain groups.

Translating tests into a test taker’s native language or using interpreters to translate test items.

Including more “performance-based” items to limit the role that language and word-choice plays in test performance.

Using multiple assessment measures to determine academic achievement and progress, and avoiding the use of test scores, in exclusion of other information, to make important decisions about students.

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